Cutting Through the Data Clouds to Build Strong Customer Relationships

The goals of building an e-commerce customer lifecycle platform are 1) to make sense of the data available; and 2) to create a customer-centric data profile so you can put customers first. Taking this approach to humanizing analytics -- that is, looking at various data sources as people instead of purchase orders -- can dramatically improve your customer relationships.

Successful companies today run largely on data and analytics. The more precisely you can measure the various aspects of your business, the better you can fine-tune its performance. With e-commerce companies especially, everything can be measured, which has provided some amazing opportunities to improve performance -- but sometimes it can lead to issues.

Managers either feel data overload as a result of a firehose of disparate information coming at them, or they focus too much on the numbers and forget that they're really dealing with people. In an effort to make sense of the vast amounts of data available and to ensure you don't lose the human aspect of your business, consider building an e-commerce customer lifecycle platform.

The ECLP is built around the five key questions any e-commerce company needs to answer to ensure success:

How do you get people to your website? (acquisition)

How do you get them to buy? (conversion)

How do you get them the product (fulfillment)

How do you get them to buy again? (retention)

How do you measure and optimize these activities? (measurement)

Most companies have systems (automated technology) and processes (manual human workflow) in place to facilitate each of these five areas of the ECLP. The problem is that these systems and processes rarely communicate with each other, and many businesses operate without a clear picture across the customer relationship.

At the core, your ECLP is about taking the data collected and aggregating it into a central source -- the customer dashboard. This customer dashboard is built around your individual customers into customer stories. By building your analytics around customer stories, you capture a bigger-picture view that helps you better understand your customers, their needs, and how your brand can better serve them. Specifically, once you've established your ECLP, you'll be able to do the following:

Determine which marketing channels are most profitable for your business.

Identify which customer groups purchase the most products.

Analyze website content to determine what produces the highest level of customer engagement.

There are a number of ways to go about creating your ECLP.
Although the end result will different for each company, there are three elements that need to be identified before your ECLP will manifest: your customer dashboard, data sources, and means of integration.

Selecting a Customer Dashboard

Your customer dashboard is exactly what it sounds like: a database of all your customers. What differs, though, is that it should include actual and potential customers. Naturally, the data available for your actual customers will be more robust than for potential customers, but you'd be surprised by the kind of information you can gather on those considering their first purchase.

Many companies already have a few platforms in place to serve as a customer dashboard. For example, you might currently track customer profiles in your shopping cart platform, email marketing platform, etc.

In this instance, however, the goal is to consolidate these various customer profiles into a single source to create a holistic and human story for each customer. The first step to take is choosing where you want your consolidated customer dashboard to live. Consider one of the following options:

Spreadsheet

Although often overlooked, the spreadsheet is one of the most robust data management platforms available and can easily serve as a customer dashboard. That said, it's not for the faint of heart.

You'll need someone who's comfortable spending time with spreadsheets, using obscure functions to merge data, and combing through numbers to find potential insights. It isn't the most sophisticated option available, but it's certainly worth considering given its flexibility and low-cost.

Custom SQL Database

For companies with available developer/programmer resources, creating a custom SQL database is a fantastic option. Using a custom database will give you as much flexibility as a spreadsheet but more advanced querying capabilities that might help build insights and correlations between your data sources.

Similar to a spreadsheet, however, this option works only for companies that have someone comfortable in this environment and willing to put in the work to build an ECLP.

Shopping Cart Platform

One would assume most e-commerce companies have some sort of shopping cart or e-commerce platform in place to capture customer order data. Many shopping cart platforms provide the user the ability to add custom fields to customer profiles.

With the right integration, this provides the ability to build a rich customer story in one of the most important data sources: where your customers actually are purchasing products.

Marketing Automation Platform

An increasing number of e-commerce companies are expanding beyond basic email newsletters into the world of marketing automation. Marketing automation delivers tailored email content to customers based on a wide range of rules and systems.

This ensures higher engagement with customers via email and provides a rich source of information tied to one of the most important customer identifiers: email addresses. Similar to many shopping cart platforms, some marketing automation platforms allow custom fields to be added to customer profiles.

Customer Relationship Management Platform

Although often pegged as a sales tool for service companies and not necessarily e-commerce, CRMs provide highly customizable profiling capabilities around individual customers.

Similar to shopping carts and marketing automation platforms, the right CRM, with appropriate integration, will append additional information to customer profiles.

Identifying Your Data Sources

Once you've chosen a customer dashboard and where you'll consolidate customer data, the next step is to identify exactly where all that data is coming from. Recall the five questions posed in the e-commerce customer lifecycle framework when thinking about which data sources to aggregate. Based on that model, consider the following:

Acquisition

Acquisition is how you drive traffic to your website. It relates directly to your online and offline marketing channels, but the most common include search engine optimization, paid online advertising, social media and public relations.

Conversion

Conversion is about how you turn visitors into customers. This includes content and the design of your website, as well as additional outside sales channels. When evaluating content and design, measure conversion funnels that provide insight into when potential customers drop out of the buying process.

Fulfillment

Fulfillment is how you deliver products to your customers. Much of this data stems from shopping carts or e-commerce platforms such as order status. In addition, pull in data on operational costs associated with fulfillment, such as shipping, packaging and warehousing.

Retention

Retention is about how you service customers and get them to come back for additional purchases. We see support systems and email marketing systems as the two primary data sources for this phase.

Measurement

Naturally, measure at each step in this framework. However, measurement can provide data on top of data. Examples include conversion optimization and A/B testing platforms, as well as social-graphing customer profiles using email addresses or social media handles.

Pulling It All Together

Once you've identified the data sources to aggregate and the respective platforms from which you'll pull data, the final step is to figure out the method by which that data will be extracted and aggregated by your customer dashboard. Following are three potential methods for aggregating data:

Manual Export/Import

Although likely to be incredibly painstaking, there is always the option of aggregating data manually. By designing a workflow/process for someone in your team to manage, you can set up a schedule to export data from each data source and import it into your customer dashboard.

This is a reliable first step in prototyping an ECLP, as it allows you to figure out which data is most important and how you'll best benefit from the ECLP. When you find yourself doing something this tedious, it helps you ruthlessly prioritize which data is most important -- a great way to ensure your ECLP doesn't get bloated.

API Data Syncing

There are a number of great tools available that sync data between two or more platforms via API integrations. Using one of these syncing platforms provides the quickest way of building an ECLP without the headache involved in doing things manually.

One drawback, however, is that you're limited by the APIs your sync platform supports. In other words, if you want to pull from a data source they don't have, you're out of luck.

Custom API Integration

The most robust method of building your ECLP is to work with a developer capable of creating custom integrations between your data sources and customer dashboard via the APIs your data sources provide.

This will ensure getting exactly the data wanted, but it likely will prove the most costly. However, there are a number of APIs for APIs that make developing these integrations easier -- and apparently more meta!

In the end, remember that the goals of building an ECLP are 1) to make sense of the data available; and 2) to create a customer-centric data profile so you can put customers first.

Taking this approach to humanizing analytics -- that is, looking at various data sources as people instead of purchase orders -- can dramatically improve your customer relationships.

Ross Beyeler is the founder of
Growth Spark, which helps e-commerce companies design interfaces that convert visitors into customers, implement technology to streamline operations and use analytics to guide marketing decisions.